Sr Analyst Model Risk & AI, Data Science and Actuarial Model Validation

Sr Analyst Model Risk Management - KM07AE

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Sr Analyst Model Risk & AI, Data Science and Actuarial Model Validation

Overview of the Position

The Hartford’s Model Risk Management function seeks a Senior Analyst to join a talented and high-performing Model Risk Management team. The successful candidate will lead efforts for the review and validation of models across the enterprise. A variety of modeling methodologies are employed at The Hartford including advanced analytics and predictive models in Data Science and traditional Actuarial and business function methods.

Models are used throughout The Hartford for a variety of purposes, including product pricing, asset pricing and valuation, risk management, reporting, economic capital, accounting, and operational functions.

The Model Risk Management team manages model risk across The Hartford by validating these models, implementing consistent policies and standards, and maintaining appropriate model oversight. As part of the team, this role will focus on validating models throughout The Hartford and reporting results to key internal stakeholders.

Through the validation process, supplementary responsibilities include educating on modeling best practices and spreading model risk awareness across the enterprise.  Additionally, this role will also support the maintenance of the enterprise model risk management framework, including policies, standards, procedures, and governance processes.

Responsibilities:

• Perform model validations of critical models used across The Hartford’s functional areas and lines of business to ensure models are performing effectively and efficiently Ensure model calculations are accurate and appropriate for intended use
• Assess key data inputs and assumptions for accuracy and appropriateness
• Review model outputs for accuracy and appropriate downstream usage
• Deliver effective challenge to key modeling elements such as inputs, calculations, outputs, conceptual soundness, monitoring & controls, documentation, etc.
• Identify findings and recommendations to mitigate model risk, compile clear, concise and well-structured model validation reports, perform in-depth assessments of model risk, perform governance accountabilities related to findings tracking, remediation testing, and validation
• Perform governance accountabilities related to findings tracking, remediation testing, and validation
• Strengthen partnerships with Data Science and Actuarial teams to keep model risk practices aligned with the proliferation and sophistication of modeling, promote proactive risk management, and share best practices.
• Assist in improving The Hartford’s Model Risk Management function, by monitoring external environment, implementing best practices, recommending process improvements, and evolving standards/guidelines

Qualifications:

• Advanced degree in Statistics, Applied Mathematics, Actuarial Science, Computational Science, Quantitative Economics, Engineering, or similar applied analytical field
• 5+ years of P&C, Group, Life or related insurance product experience
• ACAS/ASA or FCAS/FSA designation and/or CFA preferred
• Familiarity with different programming languages/technology platforms including Excel/VBA, R, Python, SAS/SQL, C++, Java
• Practical experience with predictive modeling / data science techniques preferred
• Project management skills an important plus
• Familiarity with Supervisory Guidance on Model Risk Management (SR 11-7/ SR 26-2, PRA SS1/23) viewed as favorable
• Ability to act independently with proactive self-directed accountability and demonstrated experience and consistency in meeting deadlines while adapting to shifting priorities
• Strong analytical, critical and investigative thinking skills
• Demonstrated commitment to lifelong learning with a strong desire for continuous development to keep pace with evolving modeling techniques and AI technologies.
• Solution oriented creativity, innovative thinking, and challenging the status quo.
• Excellent communication and collaboration skills, with the ability to explain complex technical concepts to non-technical stakeholders across the enterprise.

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